NPF: Network propagation for protein function prediction

Zhao, Bihai; Zhi-hong, Zhang; Jiang, Meiping; Hu, Sai; Luo, Yingchun; Wang, Lei

doi:10.21203/rs.3.rs-16452/v3

Cited by 2 publications

(2 citation statements)

References 29 publications

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“…The precision reached by these tools is still low (<0.5), even if some of them include protein-protein interaction data and the GO terms associated with interactors. The inclusion of various sources of data such as coexpression data may lead to improvements ( 76 ). It would be interesting to compare the results of computational methods with the results of manual data mining to evaluate if both approaches can complement or enrich each other.…”

Section: Discussionmentioning

confidence: 99%

Functionathon: a manual data mining workflow to generate functional hypotheses for uncharacterized human proteins and its application by undergraduate students

et al. 2021

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About 10% of human proteins have no annotated function in protein knowledge bases. A workflow to generate hypotheses for the function of these uncharacterized proteins has been developed, based on predicted and experimental information on protein properties, interactions, tissular expression, subcellular localization, conservation in other organisms, as well as phenotypic data in mutant model organisms. This workflow has been applied to seven uncharacterized human proteins (C6orf118, C7orf25, CXorf58, RSRP1, SMLR1, TMEM53 and TMEM232) in the frame of a course-based undergraduate research experience named Functionathon organized at the University of Geneva to teach undergraduate students how to use biological databases and bioinformatics tools and interpret the results. C6orf118, CXorf58 and TMEM232 were proposed to be involved in cilia-related functions; TMEM53 and SMLR1 were proposed to be involved in lipid metabolism and C7orf25 and RSRP1 were proposed to be involved in RNA metabolism and gene expression. Experimental strategies to test these hypotheses were also discussed. The results of this manual data mining study may contribute to the project recently launched by the Human Proteome Organization (HUPO) Human Proteome Project aiming to fill gaps in the functional annotation of human proteins. Database URL: http://www.nextprot.org

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Section: Discussionmentioning

confidence: 99%

Functionathon: a manual data mining workflow to generate functional hypotheses for uncharacterized human proteins and its application by undergraduate students

et al. 2021

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“…In this paper, we construct a heterogeneous biological network with the integration of PPI networks and multiple biological data, including protein complexes and protein-domain association data. On this basis, we design a novel protein function prediction method named PHN (Propagate on Heterogeneous Networks) by applying the propagation algorithm [14] on the heterogeneous biological network. To evaluate the performance of PHN, we apply our method on the Saccharomyces cerevisiae PPI network.…”

Section: Methods For Experimentally Determining Protein Function Such...mentioning

confidence: 99%

Construction of Heterogeneous Biological Networks and Its Application

Luo

Zhao

Xiong

et al. 2022

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Background: Accurate annotation of protein function is the key to understanding life at the molecular level and has great implications for biomedicine and pharmaceuticals. The rapid developments of high-throughput technologies have generated huge amounts of protein-protein interaction (PPI) data, which prompts the emergence of computational methods to determine protein function. Plagued by errors and noises hidden in PPI data, these computational methods have undertaken to focus on the prediction of functions by integrating the topology of protein interaction networks and multi-source biological data. Despite effective improvement of these computational methods, it is still challenging to build a suitable network model for integrating multi-omics data. ResultsIn this paper, we constructed a heterogeneous biological network by initially integrating original protein interaction networks, protein-domain association data and protein complexes. To prove the effectiveness of the heterogeneous biological network, we applied the propagation algorithm on this network, and proposed a novel iterative model, named PHN (Propagate on Heterogeneous Biological Networks) to score and rank functions in descending order from all functional partners and selected the first L of them as candidates to annotate the target protein. Our comprehensive experimental results demonstrated that PHN outperformed six other competing approaches using cross validation. Experimental results indicated that PHN performs significantly better than competing methods and improves the AUROC (Area Under the Receiver-Operating Curve) by no less than 32%.Conclusions:We demonstrated that integrating multi-source data into a heterogeneous biological network can preserve the complex relationship among multi-omics data and improve the prediction accuracy of protein function by getting rid of the the constraints of errors in PPI networks effectively. PHN, our proposed method, is effective for protein function prediction.

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NPF: Network propagation for protein function prediction

Cited by 2 publications

References 29 publications

Functionathon: a manual data mining workflow to generate functional hypotheses for uncharacterized human proteins and its application by undergraduate students

Functionathon: a manual data mining workflow to generate functional hypotheses for uncharacterized human proteins and its application by undergraduate students

Construction of Heterogeneous Biological Networks and Its Application

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